With　the　emergence　of　intelligent　terminals,　the　Internet　of　Vehicles　(IoV)　has　been　drawing　great　attention　by　taking　advantage　of　mobile　communication　technologies.　However,　high　computation　complexity,　collaboration　communication　overhead　and　limited　network　bandwidths　bring　severe　challenges　to　the　provision　of　latency-sensitive　IoV　services.　To　overcome　these　problems,　we　design　a　cloud-edge　cooperative　content-delivery　strategy　in　asymmetrical　IoV　environments　to　minimize　network　latency　by　providing　optimal　computing,　caching　and　communication　resource　allocation.　We　abstract　the　joint　allocation　issue　of　heterogeneous　resources　as　a　queuing　theory-based　latency　minimization　objective.　Next,　a　new　deep　reinforcement　learning　(DRL)　scheme　works　in　each　network　node　to　achieve　optimal　content　caching　and　request　routing　on　the　basis　of　the　perceptive　request　history　and　network　state.　Extensive　simulations　show　that　our　proposed　strategy　has　lower　network　latency　compared　with　the　current　solutions　in　the　cloud-edge　collaboration　system　and　converges　fast　under　different　scenarios.